Overview

Dataset statistics

Number of variables57
Number of observations6029
Missing cells2087
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 MiB
Average record size in memory456.0 B

Variable types

Categorical36
Numeric21

Alerts

accommodates is highly correlated with bedrooms and 1 other fieldsHigh correlation
bedrooms is highly correlated with accommodates and 2 other fieldsHigh correlation
beds is highly correlated with accommodates and 1 other fieldsHigh correlation
availability_30 is highly correlated with availability_365High correlation
availability_365 is highly correlated with availability_30High correlation
number_of_reviews is highly correlated with reviews_per_month and 1 other fieldsHigh correlation
number_of_reviews_ltm is highly correlated with number_of_reviews_l30d and 2 other fieldsHigh correlation
number_of_reviews_l30d is highly correlated with number_of_reviews_ltm and 2 other fieldsHigh correlation
review_scores_rating is highly correlated with review_scores_accuracy and 1 other fieldsHigh correlation
review_scores_accuracy is highly correlated with review_scores_rating and 1 other fieldsHigh correlation
review_scores_cleanliness is highly correlated with review_scores_rating and 1 other fieldsHigh correlation
reviews_per_month is highly correlated with number_of_reviews and 2 other fieldsHigh correlation
host_since_years is highly correlated with first_review_yearsHigh correlation
first_review_years is highly correlated with number_of_reviews and 1 other fieldsHigh correlation
last_review_years is highly correlated with number_of_reviews_ltm and 1 other fieldsHigh correlation
bathrooms_count is highly correlated with bedroomsHigh correlation
amenity_hot_water is highly correlated with amenity_dishes_and_silverware and 1 other fieldsHigh correlation
amenity_carbon_monoxide_alarm is highly correlated with amenity_smoke_alarmHigh correlation
amenity_cooking_basics is highly correlated with amenity_coffee_maker and 5 other fieldsHigh correlation
amenity_smoke_alarm is highly correlated with amenity_carbon_monoxide_alarmHigh correlation
amenity_coffee_maker is highly correlated with amenity_cooking_basics and 2 other fieldsHigh correlation
amenity_bed_linens is highly correlated with amenity_extra_pillows_and_blanketsHigh correlation
amenity_oven is highly correlated with amenity_cooking_basics and 3 other fieldsHigh correlation
amenity_extra_pillows_and_blankets is highly correlated with amenity_bed_linensHigh correlation
amenity_stove is highly correlated with amenity_cooking_basics and 3 other fieldsHigh correlation
amenity_dishes_and_silverware is highly correlated with amenity_hot_water and 6 other fieldsHigh correlation
amenity_refrigerator is highly correlated with amenity_hot_water and 6 other fieldsHigh correlation
amenity_kitchen is highly correlated with amenity_cooking_basicsHigh correlation
amenity_microwave is highly correlated with amenity_dishes_and_silverware and 1 other fieldsHigh correlation
host_response_rate is highly correlated with host_acceptance_rateHigh correlation
host_acceptance_rate is highly correlated with host_response_rateHigh correlation
accommodates is highly correlated with bedrooms and 2 other fieldsHigh correlation
bedrooms is highly correlated with accommodates and 2 other fieldsHigh correlation
beds is highly correlated with accommodates and 1 other fieldsHigh correlation
price is highly correlated with bathrooms_countHigh correlation
number_of_reviews is highly correlated with reviews_per_month and 1 other fieldsHigh correlation
number_of_reviews_ltm is highly correlated with number_of_reviews_l30d and 1 other fieldsHigh correlation
number_of_reviews_l30d is highly correlated with number_of_reviews_ltm and 1 other fieldsHigh correlation
review_scores_rating is highly correlated with review_scores_accuracy and 1 other fieldsHigh correlation
review_scores_accuracy is highly correlated with review_scores_rating and 1 other fieldsHigh correlation
review_scores_cleanliness is highly correlated with review_scores_rating and 1 other fieldsHigh correlation
reviews_per_month is highly correlated with number_of_reviews and 2 other fieldsHigh correlation
host_since_years is highly correlated with first_review_yearsHigh correlation
first_review_years is highly correlated with number_of_reviews and 1 other fieldsHigh correlation
bathrooms_count is highly correlated with accommodates and 2 other fieldsHigh correlation
amenity_hot_water is highly correlated with amenity_dishes_and_silverware and 1 other fieldsHigh correlation
amenity_carbon_monoxide_alarm is highly correlated with amenity_smoke_alarmHigh correlation
amenity_cooking_basics is highly correlated with amenity_coffee_maker and 5 other fieldsHigh correlation
amenity_smoke_alarm is highly correlated with amenity_carbon_monoxide_alarmHigh correlation
amenity_coffee_maker is highly correlated with amenity_cooking_basics and 2 other fieldsHigh correlation
amenity_bed_linens is highly correlated with amenity_extra_pillows_and_blanketsHigh correlation
amenity_oven is highly correlated with amenity_cooking_basics and 3 other fieldsHigh correlation
amenity_extra_pillows_and_blankets is highly correlated with amenity_bed_linensHigh correlation
amenity_stove is highly correlated with amenity_cooking_basics and 3 other fieldsHigh correlation
amenity_dishes_and_silverware is highly correlated with amenity_hot_water and 6 other fieldsHigh correlation
amenity_refrigerator is highly correlated with amenity_hot_water and 6 other fieldsHigh correlation
amenity_kitchen is highly correlated with amenity_cooking_basicsHigh correlation
amenity_microwave is highly correlated with amenity_dishes_and_silverware and 1 other fieldsHigh correlation
accommodates is highly correlated with bedrooms and 1 other fieldsHigh correlation
bedrooms is highly correlated with accommodates and 1 other fieldsHigh correlation
beds is highly correlated with accommodates and 1 other fieldsHigh correlation
number_of_reviews is highly correlated with reviews_per_monthHigh correlation
number_of_reviews_ltm is highly correlated with number_of_reviews_l30dHigh correlation
number_of_reviews_l30d is highly correlated with number_of_reviews_ltmHigh correlation
review_scores_rating is highly correlated with review_scores_accuracy and 1 other fieldsHigh correlation
review_scores_accuracy is highly correlated with review_scores_rating and 1 other fieldsHigh correlation
review_scores_cleanliness is highly correlated with review_scores_rating and 1 other fieldsHigh correlation
reviews_per_month is highly correlated with number_of_reviewsHigh correlation
amenity_hot_water is highly correlated with amenity_dishes_and_silverware and 1 other fieldsHigh correlation
amenity_carbon_monoxide_alarm is highly correlated with amenity_smoke_alarmHigh correlation
amenity_cooking_basics is highly correlated with amenity_coffee_maker and 5 other fieldsHigh correlation
amenity_smoke_alarm is highly correlated with amenity_carbon_monoxide_alarmHigh correlation
amenity_coffee_maker is highly correlated with amenity_cooking_basics and 2 other fieldsHigh correlation
amenity_bed_linens is highly correlated with amenity_extra_pillows_and_blanketsHigh correlation
amenity_oven is highly correlated with amenity_cooking_basics and 3 other fieldsHigh correlation
amenity_extra_pillows_and_blankets is highly correlated with amenity_bed_linensHigh correlation
amenity_stove is highly correlated with amenity_cooking_basics and 3 other fieldsHigh correlation
amenity_dishes_and_silverware is highly correlated with amenity_hot_water and 6 other fieldsHigh correlation
amenity_refrigerator is highly correlated with amenity_hot_water and 6 other fieldsHigh correlation
amenity_kitchen is highly correlated with amenity_cooking_basicsHigh correlation
amenity_microwave is highly correlated with amenity_dishes_and_silverware and 1 other fieldsHigh correlation
amenity_dishes_and_silverware is highly correlated with amenity_stove and 6 other fieldsHigh correlation
amenity_stove is highly correlated with amenity_dishes_and_silverware and 3 other fieldsHigh correlation
amenity_hot_water is highly correlated with amenity_dishes_and_silverware and 1 other fieldsHigh correlation
amenity_refrigerator is highly correlated with amenity_dishes_and_silverware and 6 other fieldsHigh correlation
amenity_smoke_alarm is highly correlated with amenity_carbon_monoxide_alarmHigh correlation
room_type is highly correlated with amenity_kitchenHigh correlation
amenity_carbon_monoxide_alarm is highly correlated with amenity_smoke_alarmHigh correlation
amenity_oven is highly correlated with amenity_dishes_and_silverware and 3 other fieldsHigh correlation
amenity_extra_pillows_and_blankets is highly correlated with amenity_bed_linensHigh correlation
amenity_microwave is highly correlated with amenity_dishes_and_silverware and 1 other fieldsHigh correlation
amenity_cooking_basics is highly correlated with amenity_dishes_and_silverware and 5 other fieldsHigh correlation
amenity_coffee_maker is highly correlated with amenity_dishes_and_silverware and 2 other fieldsHigh correlation
amenity_bed_linens is highly correlated with amenity_extra_pillows_and_blanketsHigh correlation
amenity_kitchen is highly correlated with room_type and 1 other fieldsHigh correlation
host_response_time is highly correlated with host_response_rate and 2 other fieldsHigh correlation
host_response_rate is highly correlated with host_response_time and 2 other fieldsHigh correlation
host_acceptance_rate is highly correlated with host_response_time and 3 other fieldsHigh correlation
host_is_superhost is highly correlated with review_scores_ratingHigh correlation
host_total_listings_count is highly correlated with host_response_time and 3 other fieldsHigh correlation
room_type is highly correlated with amenity_cooking_basics and 5 other fieldsHigh correlation
accommodates is highly correlated with bedrooms and 2 other fieldsHigh correlation
bedrooms is highly correlated with accommodates and 2 other fieldsHigh correlation
beds is highly correlated with accommodates and 1 other fieldsHigh correlation
price is highly correlated with accommodates and 1 other fieldsHigh correlation
availability_30 is highly correlated with availability_365High correlation
availability_365 is highly correlated with availability_30High correlation
number_of_reviews is highly correlated with reviews_per_month and 1 other fieldsHigh correlation
number_of_reviews_ltm is highly correlated with number_of_reviews_l30d and 1 other fieldsHigh correlation
number_of_reviews_l30d is highly correlated with number_of_reviews_ltm and 1 other fieldsHigh correlation
review_scores_rating is highly correlated with host_is_superhost and 2 other fieldsHigh correlation
review_scores_accuracy is highly correlated with review_scores_rating and 1 other fieldsHigh correlation
review_scores_cleanliness is highly correlated with review_scores_rating and 2 other fieldsHigh correlation
instant_bookable is highly correlated with host_acceptance_rateHigh correlation
reviews_per_month is highly correlated with number_of_reviews and 2 other fieldsHigh correlation
description_host is highly correlated with host_since_yearsHigh correlation
description_neighbourhood is highly correlated with description_cleaned_lengthHigh correlation
host_since_years is highly correlated with host_total_listings_count and 2 other fieldsHigh correlation
first_review_years is highly correlated with number_of_reviews and 2 other fieldsHigh correlation
last_review_years is highly correlated with review_scores_cleanliness and 1 other fieldsHigh correlation
amenity_hot_water is highly correlated with amenity_cooking_basics and 7 other fieldsHigh correlation
amenity_carbon_monoxide_alarm is highly correlated with amenity_smoke_alarmHigh correlation
amenity_cooking_basics is highly correlated with room_type and 11 other fieldsHigh correlation
amenity_workspace is highly correlated with amenity_hangersHigh correlation
amenity_smoke_alarm is highly correlated with amenity_carbon_monoxide_alarmHigh correlation
amenity_coffee_maker is highly correlated with amenity_cooking_basics and 6 other fieldsHigh correlation
amenity_iron is highly correlated with amenity_washerHigh correlation
amenity_bed_linens is highly correlated with amenity_hot_water and 5 other fieldsHigh correlation
amenity_fire_extinguisher is highly correlated with amenity_first_aid_kitHigh correlation
amenity_oven is highly correlated with amenity_cooking_basics and 6 other fieldsHigh correlation
amenity_extra_pillows_and_blankets is highly correlated with amenity_hot_water and 6 other fieldsHigh correlation
amenity_stove is highly correlated with room_type and 10 other fieldsHigh correlation
amenity_first_aid_kit is highly correlated with amenity_fire_extinguisherHigh correlation
amenity_tv is highly correlated with room_typeHigh correlation
amenity_washer is highly correlated with room_type and 4 other fieldsHigh correlation
amenity_dishes_and_silverware is highly correlated with room_type and 10 other fieldsHigh correlation
amenity_hangers is highly correlated with amenity_hot_water and 1 other fieldsHigh correlation
amenity_refrigerator is highly correlated with amenity_hot_water and 9 other fieldsHigh correlation
amenity_kitchen is highly correlated with room_type and 6 other fieldsHigh correlation
amenity_microwave is highly correlated with amenity_hot_water and 5 other fieldsHigh correlation
description_cleaned_length is highly correlated with description_neighbourhoodHigh correlation
host_response_rate has 1045 (17.3%) missing values Missing
host_acceptance_rate has 1042 (17.3%) missing values Missing
host_response_rate has 92 (1.5%) zeros Zeros
host_acceptance_rate has 175 (2.9%) zeros Zeros
host_total_listings_count has 312 (5.2%) zeros Zeros
beds has 127 (2.1%) zeros Zeros
availability_30 has 1469 (24.4%) zeros Zeros
availability_365 has 621 (10.3%) zeros Zeros
number_of_reviews_ltm has 2280 (37.8%) zeros Zeros
number_of_reviews_l30d has 3479 (57.7%) zeros Zeros

Reproduction

Analysis started2021-10-26 00:09:53.078059
Analysis finished2021-10-26 00:12:37.329789
Duration2 minutes and 44.25 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

host_response_time
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
within_hour
4160 
missing
1045 
within_few_hours
424 
within_day
 
272
few_days
 
128

Length

Max length16
Median length11
Mean length10.5495107
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwithin_day
2nd rowwithin_day
3rd rowwithin_day
4th rowwithin_hour
5th rowwithin_hour

Common Values

ValueCountFrequency (%)
within_hour4160
69.0%
missing1045
 
17.3%
within_few_hours424
 
7.0%
within_day272
 
4.5%
few_days128
 
2.1%

Length

2021-10-26T02:12:37.472715image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:37.587685image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
within_hour4160
69.0%
missing1045
 
17.3%
within_few_hours424
 
7.0%
within_day272
 
4.5%
few_days128
 
2.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

host_response_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct42
Distinct (%)0.8%
Missing1045
Missing (%)17.3%
Infinite0
Infinite (%)0.0%
Mean0.9546107544
Minimum0
Maximum1
Zeros92
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size47.2 KiB
2021-10-26T02:12:38.652187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.75
Q11
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.158123938
Coefficient of variation (CV)0.1656423179
Kurtosis24.22508746
Mean0.9546107544
Median Absolute Deviation (MAD)0
Skewness-4.812222111
Sum4757.78
Variance0.02500317978
MonotonicityNot monotonic
2021-10-26T02:12:38.894114image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
13786
62.8%
0.98310
 
5.1%
0.99122
 
2.0%
0.9101
 
1.7%
092
 
1.5%
0.9752
 
0.9%
0.7944
 
0.7%
0.9643
 
0.7%
0.9238
 
0.6%
0.836
 
0.6%
Other values (32)360
 
6.0%
(Missing)1045
 
17.3%
ValueCountFrequency (%)
092
1.5%
0.092
 
< 0.1%
0.111
 
< 0.1%
0.171
 
< 0.1%
0.21
 
< 0.1%
0.291
 
< 0.1%
0.32
 
< 0.1%
0.339
 
0.1%
0.387
 
0.1%
0.411
 
0.2%
ValueCountFrequency (%)
13786
62.8%
0.99122
 
2.0%
0.98310
 
5.1%
0.9752
 
0.9%
0.9643
 
0.7%
0.9530
 
0.5%
0.9428
 
0.5%
0.9316
 
0.3%
0.9238
 
0.6%
0.9112
 
0.2%

host_acceptance_rate
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
MISSING
ZEROS

Distinct68
Distinct (%)1.4%
Missing1042
Missing (%)17.3%
Infinite0
Infinite (%)0.0%
Mean0.9251894927
Minimum0
Maximum1
Zeros175
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size47.2 KiB
2021-10-26T02:12:39.157989image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.5
Q10.98
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.2078186402
Coefficient of variation (CV)0.2246227847
Kurtosis12.11506862
Mean0.9251894927
Median Absolute Deviation (MAD)0
Skewness-3.547201595
Sum4613.92
Variance0.04318858723
MonotonicityNot monotonic
2021-10-26T02:12:39.397887image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13106
51.5%
0.99497
 
8.2%
0.98205
 
3.4%
0175
 
2.9%
0.97123
 
2.0%
0.9587
 
1.4%
0.9665
 
1.1%
0.6657
 
0.9%
0.7551
 
0.8%
0.9450
 
0.8%
Other values (58)571
 
9.5%
(Missing)1042
 
17.3%
ValueCountFrequency (%)
0175
2.9%
0.131
 
< 0.1%
0.141
 
< 0.1%
0.151
 
< 0.1%
0.161
 
< 0.1%
0.177
 
0.1%
0.192
 
< 0.1%
0.22
 
< 0.1%
0.232
 
< 0.1%
0.253
 
< 0.1%
ValueCountFrequency (%)
13106
51.5%
0.99497
 
8.2%
0.98205
 
3.4%
0.97123
 
2.0%
0.9665
 
1.1%
0.9587
 
1.4%
0.9450
 
0.8%
0.9333
 
0.5%
0.9242
 
0.7%
0.9136
 
0.6%

host_is_superhost
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
0
3544 
1
2485 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
03544
58.8%
12485
41.2%

Length

2021-10-26T02:12:39.649774image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:39.775068image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
03544
58.8%
12485
41.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

host_total_listings_count
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct48
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.36540056
Minimum0
Maximum129
Zeros312
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size47.2 KiB
2021-10-26T02:12:39.913991image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q38
95-th percentile87
Maximum129
Range129
Interquartile range (IQR)7

Descriptive statistics

Standard deviation24.19851276
Coefficient of variation (CV)2.129138575
Kurtosis10.75779575
Mean11.36540056
Median Absolute Deviation (MAD)2
Skewness3.328603193
Sum68522
Variance585.5680197
MonotonicityNot monotonic
2021-10-26T02:12:40.154245image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
11645
27.3%
2840
13.9%
3607
 
10.1%
4472
 
7.8%
0312
 
5.2%
5247
 
4.1%
7202
 
3.4%
6195
 
3.2%
8183
 
3.0%
9124
 
2.1%
Other values (38)1202
19.9%
ValueCountFrequency (%)
0312
 
5.2%
11645
27.3%
2840
13.9%
3607
 
10.1%
4472
 
7.8%
5247
 
4.1%
6195
 
3.2%
7202
 
3.4%
8183
 
3.0%
9124
 
2.1%
ValueCountFrequency (%)
12910
 
0.2%
124103
1.7%
1144
 
0.1%
10436
 
0.6%
10034
 
0.6%
8959
1.0%
8757
0.9%
821
 
< 0.1%
778
 
0.1%
6118
 
0.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
1
5246 
0
783 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
15246
87.0%
0783
 
13.0%

Length

2021-10-26T02:12:40.358176image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:40.474165image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
15246
87.0%
0783
 
13.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

room_type
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
entire_place
4754 
private_room
1083 
hotel_room
 
170
shared_room
 
22

Length

Max length12
Median length12
Mean length11.93995688
Min length10

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowentire_place
2nd rowentire_place
3rd rowentire_place
4th rowprivate_room
5th rowentire_place

Common Values

ValueCountFrequency (%)
entire_place4754
78.9%
private_room1083
 
18.0%
hotel_room170
 
2.8%
shared_room22
 
0.4%

Length

2021-10-26T02:12:40.628055image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:40.843963image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
entire_place4754
78.9%
private_room1083
 
18.0%
hotel_room170
 
2.8%
shared_room22
 
0.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

accommodates
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.907944933
Minimum1
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.2 KiB
2021-10-26T02:12:41.037855image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median4
Q35
95-th percentile7
Maximum15
Range14
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.793407036
Coefficient of variation (CV)0.4589130777
Kurtosis2.401037797
Mean3.907944933
Median Absolute Deviation (MAD)1
Skewness1.114189248
Sum23561
Variance3.216308799
MonotonicityNot monotonic
2021-10-26T02:12:41.603181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
41916
31.8%
21583
26.3%
3696
 
11.5%
6686
 
11.4%
5648
 
10.7%
8149
 
2.5%
7125
 
2.1%
1119
 
2.0%
1044
 
0.7%
934
 
0.6%
Other values (5)29
 
0.5%
ValueCountFrequency (%)
1119
 
2.0%
21583
26.3%
3696
 
11.5%
41916
31.8%
5648
 
10.7%
6686
 
11.4%
7125
 
2.1%
8149
 
2.5%
934
 
0.6%
1044
 
0.7%
ValueCountFrequency (%)
151
 
< 0.1%
145
 
0.1%
131
 
< 0.1%
1215
 
0.2%
117
 
0.1%
1044
 
0.7%
934
 
0.6%
8149
 
2.5%
7125
 
2.1%
6686
11.4%

bedrooms
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
1.0
3283 
2.0
2046 
3.0
586 
4.0
 
98
5.0
 
16

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row1.0
5th row3.0

Common Values

ValueCountFrequency (%)
1.03283
54.5%
2.02046
33.9%
3.0586
 
9.7%
4.098
 
1.6%
5.016
 
0.3%

Length

2021-10-26T02:12:41.843076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:41.979047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1.03283
54.5%
2.02046
33.9%
3.0586
 
9.7%
4.098
 
1.6%
5.016
 
0.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

beds
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.432244153
Minimum0
Maximum8
Zeros127
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size47.2 KiB
2021-10-26T02:12:42.160948image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q33
95-th percentile5
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.452611984
Coefficient of variation (CV)0.5972311545
Kurtosis0.8508171559
Mean2.432244153
Median Absolute Deviation (MAD)1
Skewness0.9437347023
Sum14664
Variance2.110081575
MonotonicityNot monotonic
2021-10-26T02:12:42.316891image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
11779
29.5%
21616
26.8%
31231
20.4%
4756
12.5%
5288
 
4.8%
6160
 
2.7%
0127
 
2.1%
740
 
0.7%
832
 
0.5%
ValueCountFrequency (%)
0127
 
2.1%
11779
29.5%
21616
26.8%
31231
20.4%
4756
12.5%
5288
 
4.8%
6160
 
2.7%
740
 
0.7%
832
 
0.5%
ValueCountFrequency (%)
832
 
0.5%
740
 
0.7%
6160
 
2.7%
5288
 
4.8%
4756
12.5%
31231
20.4%
21616
26.8%
11779
29.5%
0127
 
2.1%

price
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct391
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.4203019
Minimum9
Maximum836
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.2 KiB
2021-10-26T02:12:42.605249image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile43
Q175
median100
Q3143
95-th percentile271
Maximum836
Range827
Interquartile range (IQR)68

Descriptive statistics

Standard deviation81.35968066
Coefficient of variation (CV)0.6645930406
Kurtosis12.76069599
Mean122.4203019
Median Absolute Deviation (MAD)31
Skewness2.844811922
Sum738072
Variance6619.397637
MonotonicityNot monotonic
2021-10-26T02:12:42.871041image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100202
 
3.4%
120170
 
2.8%
80160
 
2.7%
90155
 
2.6%
70124
 
2.1%
130114
 
1.9%
110112
 
1.9%
60102
 
1.7%
15092
 
1.5%
5090
 
1.5%
Other values (381)4708
78.1%
ValueCountFrequency (%)
91
 
< 0.1%
101
 
< 0.1%
111
 
< 0.1%
131
 
< 0.1%
141
 
< 0.1%
162
 
< 0.1%
175
0.1%
185
0.1%
193
 
< 0.1%
2011
0.2%
ValueCountFrequency (%)
8361
 
< 0.1%
7951
 
< 0.1%
7802
< 0.1%
7781
 
< 0.1%
7501
 
< 0.1%
7151
 
< 0.1%
6782
< 0.1%
6741
 
< 0.1%
6503
< 0.1%
6082
< 0.1%

availability_30
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct31
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.383645712
Minimum0
Maximum30
Zeros1469
Zeros (%)24.4%
Negative0
Negative (%)0.0%
Memory size47.2 KiB
2021-10-26T02:12:43.073303image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7
Q315
95-th percentile29
Maximum30
Range30
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.090962095
Coefficient of variation (CV)0.9688091786
Kurtosis-0.5018798342
Mean9.383645712
Median Absolute Deviation (MAD)7
Skewness0.7789499786
Sum56574
Variance82.64559182
MonotonicityNot monotonic
2021-10-26T02:12:43.331178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
01469
24.4%
4294
 
4.9%
5252
 
4.2%
6233
 
3.9%
3225
 
3.7%
1225
 
3.7%
8218
 
3.6%
7207
 
3.4%
9201
 
3.3%
2201
 
3.3%
Other values (21)2504
41.5%
ValueCountFrequency (%)
01469
24.4%
1225
 
3.7%
2201
 
3.3%
3225
 
3.7%
4294
 
4.9%
5252
 
4.2%
6233
 
3.9%
7207
 
3.4%
8218
 
3.6%
9201
 
3.3%
ValueCountFrequency (%)
30155
2.6%
29158
2.6%
2883
1.4%
2766
1.1%
2660
 
1.0%
2555
 
0.9%
2466
1.1%
2384
1.4%
2284
1.4%
2186
1.4%

availability_365
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct366
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean210.0968652
Minimum0
Maximum365
Zeros621
Zeros (%)10.3%
Negative0
Negative (%)0.0%
Memory size47.2 KiB
2021-10-26T02:12:43.569324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q197
median243
Q3329
95-th percentile359
Maximum365
Range365
Interquartile range (IQR)232

Descriptive statistics

Standard deviation126.7468409
Coefficient of variation (CV)0.6032781157
Kurtosis-1.316832134
Mean210.0968652
Median Absolute Deviation (MAD)98
Skewness-0.409169738
Sum1266674
Variance16064.76168
MonotonicityNot monotonic
2021-10-26T02:12:43.804243image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0621
 
10.3%
36598
 
1.6%
36472
 
1.2%
33970
 
1.2%
21768
 
1.1%
30961
 
1.0%
33055
 
0.9%
32949
 
0.8%
33849
 
0.8%
36348
 
0.8%
Other values (356)4838
80.2%
ValueCountFrequency (%)
0621
10.3%
138
 
0.6%
212
 
0.2%
315
 
0.2%
411
 
0.2%
511
 
0.2%
611
 
0.2%
75
 
0.1%
85
 
0.1%
97
 
0.1%
ValueCountFrequency (%)
36598
1.6%
36472
1.2%
36348
0.8%
36236
 
0.6%
36122
 
0.4%
36024
 
0.4%
35925
 
0.4%
35823
 
0.4%
35734
 
0.6%
35632
 
0.5%

number_of_reviews
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct426
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.84707248
Minimum1
Maximum765
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.2 KiB
2021-10-26T02:12:44.071806image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q18
median35
Q398
95-th percentile263.6
Maximum765
Range764
Interquartile range (IQR)90

Descriptive statistics

Standard deviation92.05923251
Coefficient of variation (CV)1.299407714
Kurtosis7.081208729
Mean70.84707248
Median Absolute Deviation (MAD)31
Skewness2.305340421
Sum427137
Variance8474.902289
MonotonicityNot monotonic
2021-10-26T02:12:44.315649image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1389
 
6.5%
3250
 
4.1%
2239
 
4.0%
4168
 
2.8%
5158
 
2.6%
7122
 
2.0%
6116
 
1.9%
9103
 
1.7%
8103
 
1.7%
1579
 
1.3%
Other values (416)4302
71.4%
ValueCountFrequency (%)
1389
6.5%
2239
4.0%
3250
4.1%
4168
2.8%
5158
2.6%
6116
 
1.9%
7122
 
2.0%
8103
 
1.7%
9103
 
1.7%
1077
 
1.3%
ValueCountFrequency (%)
7651
< 0.1%
7261
< 0.1%
7151
< 0.1%
6821
< 0.1%
6691
< 0.1%
6431
< 0.1%
6401
< 0.1%
6391
< 0.1%
6171
< 0.1%
6101
< 0.1%

number_of_reviews_ltm
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct81
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.085254603
Minimum0
Maximum223
Zeros2280
Zeros (%)37.8%
Negative0
Negative (%)0.0%
Memory size47.2 KiB
2021-10-26T02:12:44.571554image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q38
95-th percentile27
Maximum223
Range223
Interquartile range (IQR)8

Descriptive statistics

Standard deviation10.76926815
Coefficient of variation (CV)1.76973173
Kurtosis40.51369284
Mean6.085254603
Median Absolute Deviation (MAD)1
Skewness4.245038752
Sum36688
Variance115.9771366
MonotonicityNot monotonic
2021-10-26T02:12:44.979719image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02280
37.8%
1756
 
12.5%
2403
 
6.7%
3316
 
5.2%
4223
 
3.7%
5197
 
3.3%
6179
 
3.0%
7159
 
2.6%
8125
 
2.1%
10119
 
2.0%
Other values (71)1272
21.1%
ValueCountFrequency (%)
02280
37.8%
1756
 
12.5%
2403
 
6.7%
3316
 
5.2%
4223
 
3.7%
5197
 
3.3%
6179
 
3.0%
7159
 
2.6%
8125
 
2.1%
9115
 
1.9%
ValueCountFrequency (%)
2231
< 0.1%
1191
< 0.1%
1101
< 0.1%
1041
< 0.1%
981
< 0.1%
911
< 0.1%
891
< 0.1%
881
< 0.1%
821
< 0.1%
772
< 0.1%

number_of_reviews_l30d
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct18
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.277823851
Minimum0
Maximum49
Zeros3479
Zeros (%)57.7%
Negative0
Negative (%)0.0%
Memory size47.2 KiB
2021-10-26T02:12:45.215601image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile6
Maximum49
Range49
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.204750145
Coefficient of variation (CV)1.725394421
Kurtosis43.6480162
Mean1.277823851
Median Absolute Deviation (MAD)0
Skewness3.838809933
Sum7704
Variance4.8609232
MonotonicityNot monotonic
2021-10-26T02:12:45.420484image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
03479
57.7%
1838
 
13.9%
2533
 
8.8%
3368
 
6.1%
4285
 
4.7%
5181
 
3.0%
6128
 
2.1%
797
 
1.6%
849
 
0.8%
928
 
0.5%
Other values (8)43
 
0.7%
ValueCountFrequency (%)
03479
57.7%
1838
 
13.9%
2533
 
8.8%
3368
 
6.1%
4285
 
4.7%
5181
 
3.0%
6128
 
2.1%
797
 
1.6%
849
 
0.8%
928
 
0.5%
ValueCountFrequency (%)
491
 
< 0.1%
223
 
< 0.1%
161
 
< 0.1%
142
 
< 0.1%
136
 
0.1%
128
 
0.1%
117
 
0.1%
1015
 
0.2%
928
0.5%
849
0.8%

review_scores_rating
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct133
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.703247636
Minimum2
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.2 KiB
2021-10-26T02:12:45.640387image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q14.59
median4.8
Q34.93
95-th percentile5
Maximum5
Range3
Interquartile range (IQR)0.34

Descriptive statistics

Standard deviation0.3372026231
Coefficient of variation (CV)0.0716956982
Kurtosis9.732063718
Mean4.703247636
Median Absolute Deviation (MAD)0.15
Skewness-2.487172429
Sum28355.88
Variance0.113705609
MonotonicityNot monotonic
2021-10-26T02:12:46.001012image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5964
 
16.0%
4171
 
2.8%
4.5145
 
2.4%
4.89141
 
2.3%
4.83139
 
2.3%
4.75133
 
2.2%
4.67132
 
2.2%
4.93132
 
2.2%
4.86132
 
2.2%
4.9128
 
2.1%
Other values (123)3812
63.2%
ValueCountFrequency (%)
24
 
0.1%
2.333
 
< 0.1%
2.54
 
0.1%
2.672
 
< 0.1%
335
0.6%
3.171
 
< 0.1%
3.21
 
< 0.1%
3.221
 
< 0.1%
3.252
 
< 0.1%
3.293
 
< 0.1%
ValueCountFrequency (%)
5964
16.0%
4.9933
 
0.5%
4.9845
 
0.7%
4.9795
 
1.6%
4.9685
 
1.4%
4.95113
 
1.9%
4.94120
 
2.0%
4.93132
 
2.2%
4.92108
 
1.8%
4.91122
 
2.0%

review_scores_accuracy
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct124
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7813518
Minimum2
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.2 KiB
2021-10-26T02:12:46.273854image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4.25
Q14.72
median4.88
Q34.97
95-th percentile5
Maximum5
Range3
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.3179725094
Coefficient of variation (CV)0.06650263832
Kurtosis19.60171492
Mean4.7813518
Median Absolute Deviation (MAD)0.11
Skewness-3.687751863
Sum28826.77
Variance0.1011065167
MonotonicityNot monotonic
2021-10-26T02:12:46.537586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51206
 
20.0%
4.96193
 
3.2%
4.95189
 
3.1%
4.92180
 
3.0%
4.93174
 
2.9%
4.91171
 
2.8%
4.97171
 
2.8%
4.88166
 
2.8%
4.9163
 
2.7%
4.89159
 
2.6%
Other values (114)3257
54.0%
ValueCountFrequency (%)
29
 
0.1%
2.332
 
< 0.1%
2.54
 
0.1%
2.675
 
0.1%
2.751
 
< 0.1%
331
0.5%
3.141
 
< 0.1%
3.252
 
< 0.1%
3.293
 
< 0.1%
3.338
 
0.1%
ValueCountFrequency (%)
51206
20.0%
4.9938
 
0.6%
4.98120
 
2.0%
4.97171
 
2.8%
4.96193
 
3.2%
4.95189
 
3.1%
4.94154
 
2.6%
4.93174
 
2.9%
4.92180
 
3.0%
4.91171
 
2.8%

review_scores_cleanliness
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct137
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.778002986
Minimum1.67
Maximum5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.2 KiB
2021-10-26T02:12:46.814434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1.67
5-th percentile4.19
Q14.71
median4.88
Q34.98
95-th percentile5
Maximum5
Range3.33
Interquartile range (IQR)0.27

Descriptive statistics

Standard deviation0.3170786097
Coefficient of variation (CV)0.06636216232
Kurtosis15.88250222
Mean4.778002986
Median Absolute Deviation (MAD)0.12
Skewness-3.302381786
Sum28806.58
Variance0.1005388447
MonotonicityNot monotonic
2021-10-26T02:12:47.031362image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51353
22.4%
4.96170
 
2.8%
4.92168
 
2.8%
4.93168
 
2.8%
4.95156
 
2.6%
4.94150
 
2.5%
4.97149
 
2.5%
4.9143
 
2.4%
4.88142
 
2.4%
4.91138
 
2.3%
Other values (127)3292
54.6%
ValueCountFrequency (%)
1.671
 
< 0.1%
22
 
< 0.1%
2.251
 
< 0.1%
2.331
 
< 0.1%
2.57
 
0.1%
2.674
 
0.1%
2.751
 
< 0.1%
2.832
 
< 0.1%
330
0.5%
3.111
 
< 0.1%
ValueCountFrequency (%)
51353
22.4%
4.9987
 
1.4%
4.98129
 
2.1%
4.97149
 
2.5%
4.96170
 
2.8%
4.95156
 
2.6%
4.94150
 
2.5%
4.93168
 
2.8%
4.92168
 
2.8%
4.91138
 
2.3%

instant_bookable
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
1
4152 
0
1877 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
14152
68.9%
01877
31.1%

Length

2021-10-26T02:12:47.252273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:47.379237image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
14152
68.9%
01877
31.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

reviews_per_month
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct635
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.54136341
Minimum0.01
Maximum17.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.2 KiB
2021-10-26T02:12:47.539169image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.06
Q10.38
median1.06
Q32.17
95-th percentile4.686
Maximum17.72
Range17.71
Interquartile range (IQR)1.79

Descriptive statistics

Standard deviation1.557720716
Coefficient of variation (CV)1.010612232
Kurtosis6.712072779
Mean1.54136341
Median Absolute Deviation (MAD)0.8
Skewness1.965084849
Sum9292.88
Variance2.426493828
MonotonicityNot monotonic
2021-10-26T02:12:47.830002image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1102
 
1.7%
0.0480
 
1.3%
0.1173
 
1.2%
0.0673
 
1.2%
0.0868
 
1.1%
0.161
 
1.0%
0.0561
 
1.0%
0.1457
 
0.9%
0.0355
 
0.9%
0.0955
 
0.9%
Other values (625)5344
88.6%
ValueCountFrequency (%)
0.0113
 
0.2%
0.0236
0.6%
0.0355
0.9%
0.0480
1.3%
0.0561
1.0%
0.0673
1.2%
0.0727
 
0.4%
0.0868
1.1%
0.0955
0.9%
0.161
1.0%
ValueCountFrequency (%)
17.721
< 0.1%
15.251
< 0.1%
13.521
< 0.1%
11.51
< 0.1%
111
< 0.1%
10.91
< 0.1%
10.441
< 0.1%
9.921
< 0.1%
9.661
< 0.1%
9.621
< 0.1%

description_host
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
0
3654 
1
2375 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
03654
60.6%
12375
39.4%

Length

2021-10-26T02:12:48.083865image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:48.228791image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
03654
60.6%
12375
39.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

description_neighbourhood
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
0
4282 
1
1747 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
04282
71.0%
11747
29.0%

Length

2021-10-26T02:12:48.690799image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:48.824733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
04282
71.0%
11747
29.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
Castello
1197 
Cannaregio
1188 
San Marco
742 
Dorsoduro
490 
San Polo
490 
Other values (7)
1922 

Length

Max length22
Median length9
Mean length10.01525958
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSan Polo
2nd rowSanta Croce
3rd rowSanta Croce
4th rowSan Marco
5th rowCannaregio

Common Values

ValueCountFrequency (%)
Castello1197
19.9%
Cannaregio1188
19.7%
San Marco742
12.3%
Dorsoduro490
8.1%
San Polo490
8.1%
Santa Croce432
 
7.2%
Other in Terraferma366
 
6.1%
Piave 1860361
 
6.0%
Other in Isole265
 
4.4%
Lido242
 
4.0%
Other values (2)256
 
4.2%

Length

2021-10-26T02:12:48.989667image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
san1366
14.1%
castello1197
12.3%
cannaregio1188
12.2%
marco742
 
7.6%
other631
 
6.5%
in631
 
6.5%
polo490
 
5.0%
dorsoduro490
 
5.0%
santa432
 
4.4%
croce432
 
4.4%
Other values (9)2119
21.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

host_since_years
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2012
Distinct (%)33.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.783341702
Minimum0.08493150685
Maximum12.35068493
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.2 KiB
2021-10-26T02:12:49.272520image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.08493150685
5-th percentile1.860273973
Q13.690410959
median5.835616438
Q37.605479452
95-th percentile9.916712329
Maximum12.35068493
Range12.26575342
Interquartile range (IQR)3.915068493

Descriptive statistics

Standard deviation2.504023024
Coefficient of variation (CV)0.4329716543
Kurtosis-0.7646566549
Mean5.783341702
Median Absolute Deviation (MAD)1.942465753
Skewness0.01028313058
Sum34867.76712
Variance6.270131307
MonotonicityNot monotonic
2021-10-26T02:12:49.579007image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.216438356107
 
1.8%
3.69041095959
 
1.0%
10.2410958958
 
1.0%
3.45205479542
 
0.7%
8.43835616440
 
0.7%
7.37808219239
 
0.6%
4.76712328836
 
0.6%
10.7917808234
 
0.6%
8.04657534232
 
0.5%
8.85205479532
 
0.5%
Other values (2002)5550
92.1%
ValueCountFrequency (%)
0.084931506851
< 0.1%
0.11506849322
< 0.1%
0.11780821921
< 0.1%
0.12602739731
< 0.1%
0.16438356161
< 0.1%
0.16712328771
< 0.1%
0.16986301371
< 0.1%
0.17260273972
< 0.1%
0.17534246582
< 0.1%
0.19178082192
< 0.1%
ValueCountFrequency (%)
12.350684933
 
< 0.1%
12.186301371
 
< 0.1%
11.471232881
 
< 0.1%
11.342465752
 
< 0.1%
11.323287671
 
< 0.1%
11.306849322
 
< 0.1%
11.07397262
 
< 0.1%
11.060273978
0.1%
11.052054793
 
< 0.1%
11.049315071
 
< 0.1%

first_review_years
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2032
Distinct (%)33.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.684177616
Minimum0.06849315068
Maximum10.51232877
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.2 KiB
2021-10-26T02:12:49.947775image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.06849315068
5-th percentile0.2328767123
Q12.139726027
median3.35890411
Q35.169863014
95-th percentile7.485479452
Maximum10.51232877
Range10.44383562
Interquartile range (IQR)3.030136986

Descriptive statistics

Standard deviation2.13786191
Coefficient of variation (CV)0.5802819876
Kurtosis-0.3861942257
Mean3.684177616
Median Absolute Deviation (MAD)1.482191781
Skewness0.3906147338
Sum22211.90685
Variance4.570453546
MonotonicityNot monotonic
2021-10-26T02:12:50.213651image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.117808219225
 
0.4%
0.232876712324
 
0.4%
2.30410958919
 
0.3%
3.4136986318
 
0.3%
4.52876712318
 
0.3%
3.41643835618
 
0.3%
2.51506849317
 
0.3%
5.42191780817
 
0.3%
1.51780821916
 
0.3%
2.34246575316
 
0.3%
Other values (2022)5841
96.9%
ValueCountFrequency (%)
0.068493150686
0.1%
0.071232876712
 
< 0.1%
0.073972602744
0.1%
0.076712328776
0.1%
0.079452054797
0.1%
0.082191780825
0.1%
0.084931506854
0.1%
0.087671232883
 
< 0.1%
0.093150684934
0.1%
0.095890410969
0.1%
ValueCountFrequency (%)
10.512328771
< 0.1%
10.183561641
< 0.1%
10.12602741
< 0.1%
10.106849321
< 0.1%
10.098630141
< 0.1%
10.043835621
< 0.1%
9.9643835621
< 0.1%
9.9342465751
< 0.1%
9.9095890412
< 0.1%
9.8958904111
< 0.1%

last_review_years
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1141
Distinct (%)18.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.640241118
Minimum0.06575342466
Maximum10.24109589
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.2 KiB
2021-10-26T02:12:50.564989image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.06575342466
5-th percentile0.0904109589
Q10.2712328767
median1.824657534
Q32.115068493
95-th percentile4.049315068
Maximum10.24109589
Range10.17534247
Interquartile range (IQR)1.843835616

Descriptive statistics

Standard deviation1.250201506
Coefficient of variation (CV)0.7622059294
Kurtosis2.874273078
Mean1.640241118
Median Absolute Deviation (MAD)0.5479452055
Skewness1.093646302
Sum9889.013699
Variance1.563003804
MonotonicityNot monotonic
2021-10-26T02:12:50.822841image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.10410958961
 
1.0%
0.0986301369953
 
0.9%
0.117808219250
 
0.8%
0.106849315149
 
0.8%
0.120547945248
 
0.8%
1.88219178145
 
0.7%
0.0849315068544
 
0.7%
0.0794520547943
 
0.7%
0.156164383641
 
0.7%
1.53698630141
 
0.7%
Other values (1131)5554
92.1%
ValueCountFrequency (%)
0.0657534246613
 
0.2%
0.0684931506823
0.4%
0.0712328767135
0.6%
0.0739726027418
0.3%
0.0767123287739
0.6%
0.0794520547943
0.7%
0.0821917808241
0.7%
0.0849315068544
0.7%
0.0876712328823
0.4%
0.090410958924
0.4%
ValueCountFrequency (%)
10.241095891
< 0.1%
9.2684931511
< 0.1%
8.9561643841
< 0.1%
8.8767123291
< 0.1%
8.572602741
< 0.1%
8.2191780821
< 0.1%
8.1890410961
< 0.1%
8.1397260271
< 0.1%
8.1150684932
< 0.1%
8.0684931511
< 0.1%

bathrooms_count
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.29673246
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size47.2 KiB
2021-10-26T02:12:51.035737image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31.5
95-th percentile2
Maximum4
Range3
Interquartile range (IQR)0.5

Descriptive statistics

Standard deviation0.5268418303
Coefficient of variation (CV)0.4062841385
Kurtosis3.144087273
Mean1.29673246
Median Absolute Deviation (MAD)0
Skewness1.802162049
Sum7818
Variance0.2775623141
MonotonicityNot monotonic
2021-10-26T02:12:51.205662image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
14331
71.8%
21189
 
19.7%
1.5275
 
4.6%
3159
 
2.6%
2.552
 
0.9%
418
 
0.3%
3.55
 
0.1%
ValueCountFrequency (%)
14331
71.8%
1.5275
 
4.6%
21189
 
19.7%
2.552
 
0.9%
3159
 
2.6%
3.55
 
0.1%
418
 
0.3%
ValueCountFrequency (%)
418
 
0.3%
3.55
 
0.1%
3159
 
2.6%
2.552
 
0.9%
21189
 
19.7%
1.5275
 
4.6%
14331
71.8%

amenity_hot_water
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
1
4982 
0
1047 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
14982
82.6%
01047
 
17.4%

Length

2021-10-26T02:12:51.432511image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:51.564450image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
14982
82.6%
01047
 
17.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
0
3457 
1
2572 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03457
57.3%
12572
42.7%

Length

2021-10-26T02:12:51.721211image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:51.876127image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
03457
57.3%
12572
42.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

amenity_carbon_monoxide_alarm
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
0
4619 
1
1410 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
04619
76.6%
11410
 
23.4%

Length

2021-10-26T02:12:52.038028image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:52.162978image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
04619
76.6%
11410
 
23.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

amenity_cooking_basics
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
1
3956 
0
2073 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
13956
65.6%
02073
34.4%

Length

2021-10-26T02:12:52.307874image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:52.433825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
13956
65.6%
02073
34.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

amenity_workspace
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
1
4077 
0
1952 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
14077
67.6%
01952
32.4%

Length

2021-10-26T02:12:52.570752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:52.698699image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
14077
67.6%
01952
32.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

amenity_smoke_alarm
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
0
4439 
1
1590 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
04439
73.6%
11590
 
26.4%

Length

2021-10-26T02:12:52.841595image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:52.967562image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
04439
73.6%
11590
 
26.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
0
4100 
1
1929 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
04100
68.0%
11929
32.0%

Length

2021-10-26T02:12:53.137461image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:53.272398image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
04100
68.0%
11929
32.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

amenity_coffee_maker
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
1
3535 
0
2494 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
13535
58.6%
02494
41.4%

Length

2021-10-26T02:12:53.409347image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:53.538301image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
13535
58.6%
02494
41.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

amenity_iron
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
1
4393 
0
1636 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
14393
72.9%
01636
 
27.1%

Length

2021-10-26T02:12:53.683254image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:53.895109image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
14393
72.9%
01636
 
27.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
0
4123 
1
1906 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
04123
68.4%
11906
31.6%

Length

2021-10-26T02:12:54.067870image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:54.203346image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
04123
68.4%
11906
31.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

amenity_bed_linens
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
1
3482 
0
2547 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
13482
57.8%
02547
42.2%

Length

2021-10-26T02:12:54.343273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:54.466201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
13482
57.8%
02547
42.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

amenity_fire_extinguisher
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
0
4413 
1
1616 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
04413
73.2%
11616
 
26.8%

Length

2021-10-26T02:12:54.606128image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:54.729081image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
04413
73.2%
11616
 
26.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

amenity_oven
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
0
3405 
1
2624 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03405
56.5%
12624
43.5%

Length

2021-10-26T02:12:54.871015image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:54.996970image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
03405
56.5%
12624
43.5%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

amenity_extra_pillows_and_blankets
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
0
3617 
1
2412 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
03617
60.0%
12412
40.0%

Length

2021-10-26T02:12:55.131152image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:55.253112image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
03617
60.0%
12412
40.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

amenity_stove
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
1
3211 
0
2818 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
13211
53.3%
02818
46.7%

Length

2021-10-26T02:12:55.385068image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:55.500031image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
13211
53.3%
02818
46.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

amenity_first_aid_kit
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
0
4272 
1
1757 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
04272
70.9%
11757
29.1%

Length

2021-10-26T02:12:55.631976image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:55.760911image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
04272
70.9%
11757
29.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

amenity_tv
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
1
5016 
0
1013 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
15016
83.2%
01013
 
16.8%

Length

2021-10-26T02:12:55.901865image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:56.027838image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
15016
83.2%
01013
 
16.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

amenity_washer
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
1
4259 
0
1770 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
14259
70.6%
01770
29.4%

Length

2021-10-26T02:12:56.184260image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:56.309217image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
14259
70.6%
01770
29.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

amenity_shampoo
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
1
4263 
0
1766 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
14263
70.7%
01766
29.3%

Length

2021-10-26T02:12:56.778001image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:56.900950image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
14263
70.7%
01766
29.3%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

amenity_dishes_and_silverware
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
1
4338 
0
1691 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
14338
72.0%
01691
 
28.0%

Length

2021-10-26T02:12:57.033899image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:57.155830image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
14338
72.0%
01691
 
28.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

amenity_hangers
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
1
5262 
0
767 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
15262
87.3%
0767
 
12.7%

Length

2021-10-26T02:12:57.298284image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:57.426239image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
15262
87.3%
0767
 
12.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

amenity_refrigerator
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
1
4379 
0
1650 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
14379
72.6%
01650
 
27.4%

Length

2021-10-26T02:12:57.566181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:57.688101image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
14379
72.6%
01650
 
27.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

amenity_kitchen
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
1
5058 
0
971 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
15058
83.9%
0971
 
16.1%

Length

2021-10-26T02:12:57.827020image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:57.952994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
15058
83.9%
0971
 
16.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

amenity_microwave
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
1
3161 
0
2868 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
13161
52.4%
02868
47.6%

Length

2021-10-26T02:12:58.087922image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:58.215872image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
13161
52.4%
02868
47.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
1
5374 
0
655 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
15374
89.1%
0655
 
10.9%

Length

2021-10-26T02:12:58.354791image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:58.475257image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
15374
89.1%
0655
 
10.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
1
5020 
0
1009 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
15020
83.3%
01009
 
16.7%

Length

2021-10-26T02:12:58.645178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:58.770138image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
15020
83.3%
01009
 
16.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

name_cleaned_length
Real number (ℝ≥0)

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.277823851
Minimum0
Maximum15
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size47.2 KiB
2021-10-26T02:12:58.888057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q14
median5
Q37
95-th percentile9
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.106867978
Coefficient of variation (CV)0.399192553
Kurtosis-0.5003286615
Mean5.277823851
Median Absolute Deviation (MAD)2
Skewness0.1426282838
Sum31820
Variance4.438892676
MonotonicityNot monotonic
2021-10-26T02:12:59.053011image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
61042
17.3%
5948
15.7%
4872
14.5%
7844
14.0%
3736
12.2%
8563
9.3%
2560
9.3%
9275
 
4.6%
189
 
1.5%
1070
 
1.2%
Other values (5)30
 
0.5%
ValueCountFrequency (%)
01
 
< 0.1%
189
 
1.5%
2560
9.3%
3736
12.2%
4872
14.5%
5948
15.7%
61042
17.3%
7844
14.0%
8563
9.3%
9275
 
4.6%
ValueCountFrequency (%)
151
 
< 0.1%
133
 
< 0.1%
127
 
0.1%
1118
 
0.3%
1070
 
1.2%
9275
 
4.6%
8563
9.3%
7844
14.0%
61042
17.3%
5948
15.7%

description_cleaned_length
Real number (ℝ≥0)

HIGH CORRELATION

Distinct187
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.8472383
Minimum0
Maximum189
Zeros48
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size47.2 KiB
2021-10-26T02:12:59.263905image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile37
Q1102
median148
Q3158
95-th percentile169
Maximum189
Range189
Interquartile range (IQR)56

Descriptive statistics

Standard deviation43.2820381
Coefficient of variation (CV)0.3385449593
Kurtosis0.326246107
Mean127.8472383
Median Absolute Deviation (MAD)14
Skewness-1.198582934
Sum770791
Variance1873.334822
MonotonicityNot monotonic
2021-10-26T02:12:59.478840image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
153177
 
2.9%
155175
 
2.9%
156168
 
2.8%
161166
 
2.8%
154161
 
2.7%
157158
 
2.6%
152158
 
2.6%
158156
 
2.6%
159141
 
2.3%
160134
 
2.2%
Other values (177)4435
73.6%
ValueCountFrequency (%)
048
0.8%
11
 
< 0.1%
23
 
< 0.1%
33
 
< 0.1%
44
 
0.1%
53
 
< 0.1%
69
 
0.1%
73
 
< 0.1%
89
 
0.1%
105
 
0.1%
ValueCountFrequency (%)
1891
 
< 0.1%
1881
 
< 0.1%
1872
 
< 0.1%
1864
 
0.1%
1833
 
< 0.1%
1826
0.1%
1814
 
0.1%
1806
0.1%
17911
0.2%
17814
0.2%

name_touristy
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size47.2 KiB
0
4812 
1
1217 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
04812
79.8%
11217
 
20.2%

Length

2021-10-26T02:12:59.717222image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-26T02:12:59.846177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
04812
79.8%
11217
 
20.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2021-10-26T02:12:26.340577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:10:16.443167image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:10:23.084020image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:10:29.833936image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:10:37.350947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:10:44.604594image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:10:53.298625image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:00.521500image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:08.512479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:14.462945image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:19.860269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:25.236051image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:31.805466image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:37.281095image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:42.422168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:47.600039image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:53.435305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:59.376866image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:12:05.518827image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:12:12.450770image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:12:21.087778image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:12:26.558439image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:10:17.076052image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:10:23.369857image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:10:30.058804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:10:37.653953image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:10:44.951398image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:10:53.710392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:00.848315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:08.781323image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:14.722796image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:20.064161image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:25.470923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:32.225677image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:37.489003image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:42.639854image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:47.847145image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:53.666769image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:59.788952image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:12:05.729703image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:12:12.978477image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:12:21.339220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:12:26.782243image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:10:17.493054image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:10:23.951119image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:10:30.367334image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:10:37.902016image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:10:45.278208image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:10:54.061194image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:01.146147image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:09.077703image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:14.972653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:20.262045image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:25.723144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:32.516506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:37.709874image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:43.136358image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:48.110032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:53.908223image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:12:00.275673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:12:05.941898image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:12:13.339072image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:12:21.567089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:12:27.026756image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:10:17.851848image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:10:24.190960image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:10:30.630205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:10:38.189049image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:10:45.610017image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:10:54.394001image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:01.508934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:09.322283image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:15.240487image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:20.467965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:25.951046image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:32.878310image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:37.932807image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:43.368225image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:48.376935image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:54.134113image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:12:00.580319image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:12:06.271688image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:12:13.814800image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:12:21.827944image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:12:27.283243image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:10:18.182662image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:10:24.407837image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:10:30.884040image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:10:38.437907image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:10:46.026777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:10:54.838747image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:01.841748image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:09.599103image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:15.484377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-26T02:11:20.674883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-10-26T02:12:26.113700image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-10-26T02:13:00.129915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-26T02:13:03.996837image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-26T02:13:08.789256image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-26T02:13:12.674321image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-10-26T02:13:13.723923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-26T02:12:32.922016image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-10-26T02:12:35.822758image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-10-26T02:12:36.142611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

host_response_timehost_response_ratehost_acceptance_ratehost_is_superhosthost_total_listings_counthost_identity_verifiedroom_typeaccommodatesbedroomsbedspriceavailability_30availability_365number_of_reviewsnumber_of_reviews_ltmnumber_of_reviews_l30dreview_scores_ratingreview_scores_accuracyreview_scores_cleanlinessinstant_bookablereviews_per_monthdescription_hostdescription_neighbourhoodneighbourhood_cleansed_groupedhost_since_yearsfirst_review_yearslast_review_yearsbathrooms_countamenity_hot_wateramenity_host_greets_youamenity_carbon_monoxide_alarmamenity_cooking_basicsamenity_workspaceamenity_smoke_alarmamenity_private_entranceamenity_coffee_makeramenity_ironamenity_luggage_dropoff_allowedamenity_bed_linensamenity_fire_extinguisheramenity_ovenamenity_extra_pillows_and_blanketsamenity_stoveamenity_first_aid_kitamenity_tvamenity_washeramenity_shampooamenity_dishes_and_silverwareamenity_hangersamenity_refrigeratoramenity_kitchenamenity_microwaveamenity_long_term_stays_allowedamenity_air_conditioningname_cleaned_lengthdescription_cleaned_lengthname_touristy
0within_day0.70.87016.01entire_place42.04.0225.029351162004.944.964.9901.5500San Polo12.3506858.6493152.1808222.01100100011000000111010101151711
1within_day0.70.87016.01entire_place62.06.0250.02033864004.984.954.9700.6800Santa Croce12.3506857.8246585.1232882.01100100011000000111010101161641
2within_day0.70.87016.01entire_place42.04.0122.029364184004.944.944.9801.5400Santa Croce12.3506859.8684932.9534252.01100100011000000111010101161550
3within_hour0.80.2902.01private_room41.02.0200.022341411114.824.834.9003.3610San Marco11.34246610.1068491.6602741.01000100110010000110111111191610
4within_hour1.01.0013.01entire_place63.03.0280.0723677514.824.804.7511.4200Cannaregio11.3232884.5068491.6547952.0001001011001000011000000017421
5within_few_hours0.81.00012.01entire_place62.04.0200.0213516003.002.753.2510.2801Castello11.0602741.8246588.1397262.01100101000000000101010100151380
6within_few_hours0.81.00012.01entire_place62.02.0210.01733561204.104.424.3610.7001San Marco11.0602747.2301371.8931512.01000101000000000101010100141491
7within_hour1.01.0014.01hotel_room21.01.065.014334279834.924.944.9912.2800San Polo11.01643810.1260270.1369861.01100100111010001101011001181570
8within_hour1.01.0012.01entire_place53.03.0136.000145004.804.924.8611.2200Dorsoduro11.0136999.8328771.6164382.00000100010010001111010101131620
9within_hour1.01.0013.01entire_place32.02.0105.01334879734.814.834.8210.9510Dorsoduro10.9863016.8986302.1890411.01101100110001000100111111131680

Last rows

host_response_timehost_response_ratehost_acceptance_ratehost_is_superhosthost_total_listings_counthost_identity_verifiedroom_typeaccommodatesbedroomsbedspriceavailability_30availability_365number_of_reviewsnumber_of_reviews_ltmnumber_of_reviews_l30dreview_scores_ratingreview_scores_accuracyreview_scores_cleanlinessinstant_bookablereviews_per_monthdescription_hostdescription_neighbourhoodneighbourhood_cleansed_groupedhost_since_yearsfirst_review_yearslast_review_yearsbathrooms_countamenity_hot_wateramenity_host_greets_youamenity_carbon_monoxide_alarmamenity_cooking_basicsamenity_workspaceamenity_smoke_alarmamenity_private_entranceamenity_coffee_makeramenity_ironamenity_luggage_dropoff_allowedamenity_bed_linensamenity_fire_extinguisheramenity_ovenamenity_extra_pillows_and_blanketsamenity_stoveamenity_first_aid_kitamenity_tvamenity_washeramenity_shampooamenity_dishes_and_silverwareamenity_hangersamenity_refrigeratoramenity_kitchenamenity_microwaveamenity_long_term_stays_allowedamenity_air_conditioningname_cleaned_lengthdescription_cleaned_lengthname_touristy
6019within_hour1.000.7200.01private_room21.01.040.0213301115.05.05.001.011Piave 18600.1917810.1041100.1041101.0000000000000000010000000114820
6020within_hour0.940.98017.01entire_place41.01.0106.0663335.05.05.013.001San Polo5.2712330.1041100.1205481.01001000100101010101101101131760
6021within_few_hours1.001.0000.01entire_place42.04.0180.063261115.05.05.001.011Santa Croce0.1260270.0849320.0849322.011010011101010101101011111400
6022within_hour1.001.0015.01private_room21.01.081.063186665.05.05.016.000San Marco6.4246580.0986300.1095891.01000100001100000001000001151000
6023within_day0.911.0000.01private_room11.01.047.011662224.54.54.012.010Other in Isole0.1178080.0986300.0821921.00000000000000000010000000021480
6024within_hour1.001.0000.01entire_place41.04.0179.0303291115.05.05.001.000Dorsoduro0.1150680.0876710.0876712.01001100110101111011111111121530
6025missingNaNNaN00.01entire_place41.02.092.0141641115.05.05.001.000San Polo0.1150680.0767120.0767121.01101100110101110110111101171611
6026within_hour0.990.970100.01entire_place21.01.093.001771113.02.03.011.001Cannaregio10.7917810.0986300.0986301.01001000110101010101101111171610
6027within_hour0.891.0004.01private_room11.01.030.0253401115.05.05.011.011San Lorenzo XXV Aprile0.1726030.0931510.0931511.0100001000101000010100000117910
6028missingNaN1.0000.01private_room21.01.035.0193541114.04.05.001.011Piave 18600.0849320.0712330.0712331.0000000000000000000000000106360